In-situ monitoring of nitrate in industrial wastewater using Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) coupled with chemometrics methods

Gan, Fangqun; Wu, Ke; Ma, Fang; Wei, Cuilan; Du, Changwen

doi:10.1016/j.heliyon.2022.e12423

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…Several researchers [20,84,85] have used SVR to predict SOC. When dealing with linear and nonlinear multivariate problems in regression classification, the least squares support vector regression (LS-SVR) method is preferred over quadratic programming due to its simplicity.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Using Various Models for Predicting Soil Organic Carbon Based on DRIFT-FTIR and Chemical Analysis

Thabit,

Negim,

AbdelRahman

et al. 2024

Soil Systems

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Soil organic carbon (SOC) is a crucial factor influencing soil quality and fertility. In this particular investigation, we aimed to explore the possibility of using diffuse reflectance infrared fourier transform spectroscopy (DRIFT-FTIR) in conjunction with machine-learning models, such as partial least squares regression (PLSR), artificial neural networks (ANN), support vector regression (SVR) and random forest (RF), to estimate SOC in Sohag, Egypt. To achieve this, we collected a total of ninety surface soil samples from various locations in Sohag and estimated the total organic carbon content using both the Walkley-Black method and DRIFT-FTIR spectroscopy. Subsequently, we used the spectral data to develop regression models using PLSR, ANN, SVR, and RF. To evaluate the performance of these models, we used several evaluation parameters, including root mean square error (RMSE), coefficient of determination (R2), and ratio of performance deviation (RPD). Our survey results revealed that the PLSR model had the most favorable performance, yielding an R2 value of 0.82 and an RMSE of 0.006%. In contrast, the ANN, SVR, and RF models demonstrated moderate to poor performance, with R2 values of 0.53, 0.27, and 0.18, respectively. Overall, our study highlights the potential of combining DRIFT-FTIR spectroscopy with multivariate analysis techniques to predict SOC in Sohag, Egypt. However, additional studies and research are needed to improve the accuracy or predictability of machine-learning models incorporated into DRIFT-FTIR analysis and to compare DRIFT-FTIR analysis techniques with conventional soil chemical measurements.

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Section: Support Vector Regressionmentioning

confidence: 99%